From the Guangdong Provincial Institute of Public Health, Guangzhou, China (H.L., J.X., T.L., X.L., W.Z.,W.M.); Shanghai Municipal Centre for Disease Control and Prevention, China (Y.G., Y.Z., F.W.); Harvard T.H. Chan School of Public Health, Boston, MA (Q.D.); Department of Health Statistics and Information Systems, World Health Organization, WHO SAGE, Geneva, Switzerland (P.K.); University of Newcastle Research Centre on Gender, Health and Ageing, Australia (P.K.); Saint Louis University College for Public Health and Social Justice, Missouri (S.H., Z.Q.); Department of Epidemiology and Biostatistics, IU School of Public Health–Bloomington, Indiana (E.N.).

From the Guangdong Provincial Institute of Public Health, Guangzhou, China (H.L., J.X., T.L., X.L., W.Z.,W.M.); Shanghai Municipal Centre for Disease Control and Prevention, China (Y.G., Y.Z., F.W.); Harvard T.H. Chan School of Public Health, Boston, MA (Q.D.); Department of Health Statistics and Information Systems, World Health Organization, WHO SAGE, Geneva, Switzerland (P.K.); University of Newcastle Research Centre on Gender, Health and Ageing, Australia (P.K.); Saint Louis University College for Public Health and Social Justice, Missouri (S.H., Z.Q.); Department of Epidemiology and Biostatistics, IU School of Public Health–Bloomington, Indiana (E.N.).

From the Guangdong Provincial Institute of Public Health, Guangzhou, China (H.L., J.X., T.L., X.L., W.Z.,W.M.); Shanghai Municipal Centre for Disease Control and Prevention, China (Y.G., Y.Z., F.W.); Harvard T.H. Chan School of Public Health, Boston, MA (Q.D.); Department of Health Statistics and Information Systems, World Health Organization, WHO SAGE, Geneva, Switzerland (P.K.); University of Newcastle Research Centre on Gender, Health and Ageing, Australia (P.K.); Saint Louis University College for Public Health and Social Justice, Missouri (S.H., Z.Q.); Department of Epidemiology and Biostatistics, IU School of Public Health–Bloomington, Indiana (E.N.).

From the Guangdong Provincial Institute of Public Health, Guangzhou, China (H.L., J.X., T.L., X.L., W.Z.,W.M.); Shanghai Municipal Centre for Disease Control and Prevention, China (Y.G., Y.Z., F.W.); Harvard T.H. Chan School of Public Health, Boston, MA (Q.D.); Department of Health Statistics and Information Systems, World Health Organization, WHO SAGE, Geneva, Switzerland (P.K.); University of Newcastle Research Centre on Gender, Health and Ageing, Australia (P.K.); Saint Louis University College for Public Health and Social Justice, Missouri (S.H., Z.Q.); Department of Epidemiology and Biostatistics, IU School of Public Health–Bloomington, Indiana (E.N.).

From the Guangdong Provincial Institute of Public Health, Guangzhou, China (H.L., J.X., T.L., X.L., W.Z.,W.M.); Shanghai Municipal Centre for Disease Control and Prevention, China (Y.G., Y.Z., F.W.); Harvard T.H. Chan School of Public Health, Boston, MA (Q.D.); Department of Health Statistics and Information Systems, World Health Organization, WHO SAGE, Geneva, Switzerland (P.K.); University of Newcastle Research Centre on Gender, Health and Ageing, Australia (P.K.); Saint Louis University College for Public Health and Social Justice, Missouri (S.H., Z.Q.); Department of Epidemiology and Biostatistics, IU School of Public Health–Bloomington, Indiana (E.N.).

From the Guangdong Provincial Institute of Public Health, Guangzhou, China (H.L., J.X., T.L., X.L., W.Z.,W.M.); Shanghai Municipal Centre for Disease Control and Prevention, China (Y.G., Y.Z., F.W.); Harvard T.H. Chan School of Public Health, Boston, MA (Q.D.); Department of Health Statistics and Information Systems, World Health Organization, WHO SAGE, Geneva, Switzerland (P.K.); University of Newcastle Research Centre on Gender, Health and Ageing, Australia (P.K.); Saint Louis University College for Public Health and Social Justice, Missouri (S.H., Z.Q.); Department of Epidemiology and Biostatistics, IU School of Public Health–Bloomington, Indiana (E.N.).

From the Guangdong Provincial Institute of Public Health, Guangzhou, China (H.L., J.X., T.L., X.L., W.Z.,W.M.); Shanghai Municipal Centre for Disease Control and Prevention, China (Y.G., Y.Z., F.W.); Harvard T.H. Chan School of Public Health, Boston, MA (Q.D.); Department of Health Statistics and Information Systems, World Health Organization, WHO SAGE, Geneva, Switzerland (P.K.); University of Newcastle Research Centre on Gender, Health and Ageing, Australia (P.K.); Saint Louis University College for Public Health and Social Justice, Missouri (S.H., Z.Q.); Department of Epidemiology and Biostatistics, IU School of Public Health–Bloomington, Indiana (E.N.).

From the Guangdong Provincial Institute of Public Health, Guangzhou, China (H.L., J.X., T.L., X.L., W.Z.,W.M.); Shanghai Municipal Centre for Disease Control and Prevention, China (Y.G., Y.Z., F.W.); Harvard T.H. Chan School of Public Health, Boston, MA (Q.D.); Department of Health Statistics and Information Systems, World Health Organization, WHO SAGE, Geneva, Switzerland (P.K.); University of Newcastle Research Centre on Gender, Health and Ageing, Australia (P.K.); Saint Louis University College for Public Health and Social Justice, Missouri (S.H., Z.Q.); Department of Epidemiology and Biostatistics, IU School of Public Health–Bloomington, Indiana (E.N.).

From the Guangdong Provincial Institute of Public Health, Guangzhou, China (H.L., J.X., T.L., X.L., W.Z.,W.M.); Shanghai Municipal Centre for Disease Control and Prevention, China (Y.G., Y.Z., F.W.); Harvard T.H. Chan School of Public Health, Boston, MA (Q.D.); Department of Health Statistics and Information Systems, World Health Organization, WHO SAGE, Geneva, Switzerland (P.K.); University of Newcastle Research Centre on Gender, Health and Ageing, Australia (P.K.); Saint Louis University College for Public Health and Social Justice, Missouri (S.H., Z.Q.); Department of Epidemiology and Biostatistics, IU School of Public Health–Bloomington, Indiana (E.N.).

From the Guangdong Provincial Institute of Public Health, Guangzhou, China (H.L., J.X., T.L., X.L., W.Z.,W.M.); Shanghai Municipal Centre for Disease Control and Prevention, China (Y.G., Y.Z., F.W.); Harvard T.H. Chan School of Public Health, Boston, MA (Q.D.); Department of Health Statistics and Information Systems, World Health Organization, WHO SAGE, Geneva, Switzerland (P.K.); University of Newcastle Research Centre on Gender, Health and Ageing, Australia (P.K.); Saint Louis University College for Public Health and Social Justice, Missouri (S.H., Z.Q.); Department of Epidemiology and Biostatistics, IU School of Public Health–Bloomington, Indiana (E.N.).

From the Guangdong Provincial Institute of Public Health, Guangzhou, China (H.L., J.X., T.L., X.L., W.Z.,W.M.); Shanghai Municipal Centre for Disease Control and Prevention, China (Y.G., Y.Z., F.W.); Harvard T.H. Chan School of Public Health, Boston, MA (Q.D.); Department of Health Statistics and Information Systems, World Health Organization, WHO SAGE, Geneva, Switzerland (P.K.); University of Newcastle Research Centre on Gender, Health and Ageing, Australia (P.K.); Saint Louis University College for Public Health and Social Justice, Missouri (S.H., Z.Q.); Department of Epidemiology and Biostatistics, IU School of Public Health–Bloomington, Indiana (E.N.).

From the Guangdong Provincial Institute of Public Health, Guangzhou, China (H.L., J.X., T.L., X.L., W.Z.,W.M.); Shanghai Municipal Centre for Disease Control and Prevention, China (Y.G., Y.Z., F.W.); Harvard T.H. Chan School of Public Health, Boston, MA (Q.D.); Department of Health Statistics and Information Systems, World Health Organization, WHO SAGE, Geneva, Switzerland (P.K.); University of Newcastle Research Centre on Gender, Health and Ageing, Australia (P.K.); Saint Louis University College for Public Health and Social Justice, Missouri (S.H., Z.Q.); Department of Epidemiology and Biostatistics, IU School of Public Health–Bloomington, Indiana (E.N.).

From the Guangdong Provincial Institute of Public Health, Guangzhou, China (H.L., J.X., T.L., X.L., W.Z.,W.M.); Shanghai Municipal Centre for Disease Control and Prevention, China (Y.G., Y.Z., F.W.); Harvard T.H. Chan School of Public Health, Boston, MA (Q.D.); Department of Health Statistics and Information Systems, World Health Organization, WHO SAGE, Geneva, Switzerland (P.K.); University of Newcastle Research Centre on Gender, Health and Ageing, Australia (P.K.); Saint Louis University College for Public Health and Social Justice, Missouri (S.H., Z.Q.); Department of Epidemiology and Biostatistics, IU School of Public Health–Bloomington, Indiana (E.N.).

From the Guangdong Provincial Institute of Public Health, Guangzhou, China (H.L., J.X., T.L., X.L., W.Z.,W.M.); Shanghai Municipal Centre for Disease Control and Prevention, China (Y.G., Y.Z., F.W.); Harvard T.H. Chan School of Public Health, Boston, MA (Q.D.); Department of Health Statistics and Information Systems, World Health Organization, WHO SAGE, Geneva, Switzerland (P.K.); University of Newcastle Research Centre on Gender, Health and Ageing, Australia (P.K.); Saint Louis University College for Public Health and Social Justice, Missouri (S.H., Z.Q.); Department of Epidemiology and Biostatistics, IU School of Public Health–Bloomington, Indiana (E.N.).

Abstract

Background and Purpose—Short-term exposure to ambient fine particulate pollution (PM2.5) has been linked to increased stroke. Few studies, however, have examined the effects of long-term exposure.

Methods—A total of 45 625 participants were interviewed and included in this study, the participants came from the Study on Global Ageing and Adult Health, a prospective cohort in 6 low- and middle-income countries. Ambient PM2.5 levels were estimated for participants’ communities using satellite data. A multilevel logistic regression model was used to examine the association between long-term PM2.5 exposure and stroke. Potential effect modification by physical activity and consumption of fruit and vegetables was assessed.

Results—The odds of stroke were 1.13 (95% confidence interval, 1.04–1.22) for each 10 μg/m3 increase in PM2.5. This effect remained after adjustment for confounding factors including age, sex, smoking, and indoor air pollution (adjusted odds ratio=1.12; 95% confidence interval, 1.04–1.21). Further stratified analyses suggested that participants with higher levels of physical activity had greater odds of stroke, whereas those with higher consumption of fruit and vegetables had lower odds of stroke. These effects remained robust in sensitivity analyses. We further estimated that 6.55% (95% confidence interval, 1.97%–12.01%) of the stroke cases could be attributable to ambient PM2.5 in the study population.

Conclusions—This study suggests that ambient PM2.5 may increase the risk of stroke and may be responsible for the astounding stroke burden in low- and middle-income countries. In addition, greater physical activity may enhance, whereas greater consumption of fruit and vegetables may mitigate the effect.

Introduction

Exposure to fine particulate matter (PM2.5) has been implicated as a cause to several adverse cardiovascular outcomes, including cerebrovascular diseases.1,2 With its small particle size, PM2.5 is capable of entering the central nervous system and can directly or indirectly trigger inflammatory processes.1 Thus, it is believed that long-term exposure to PM2.5 may increase the risk of adverse cardiovascular outcomes, such as stroke.

Few studies have explored potential effect modifiers for stroke in terms of demographic, behavioral, and dietary factors. Physical activity is beneficial to general health, but may increase air pollution exposures because breathing rates increase while exercising, resulting in more harmful health effects.3 Higher intake of antioxidant nutrients has been found to be an important effect modifier between air pollution and health outcomes.4 Fruits and vegetables are the primary dietary source of antioxidants, so higher consumption of fruit and vegetables may potentially reduce the oxidant stress effects of air pollutants on stroke.

This study was conducted using the survey data from 6 low- and middle-income countries to examine whether long-term PM2.5 exposure was associated with stroke, as well as whether physical activity and consumption of fruit and vegetables could modify this effect, we also estimated the stroke burden attributable to ambient PM2.5.

Methods

Study Population

The WHO SAGE (World Health Organization Study on Global Ageing and Adult Health) is a cohort study in 6 low- and middle-income countries: China, Ghana, India, Mexico, Russia, and South Africa.5 SAGE Wave 1, conducted from 2007 to 2010, was used in this study. The survey was conducted through a face-to-face household interview among adults aged ≥18 years using a stratified multistage random cluster sampling.6 The survey consisted of questions regarding demographic, economic, social, behavioral, and health factors. The primary sampling units were stratified by region and location (eg, urban/rural) and, within each stratum, enumeration areas were selected.5

Stroke and Air Pollution

Participants who have been diagnosed as stroke by a healthcare professional or self-reported current treatment for stroke within the past 12 months were classified as stroke cases.

We estimated the yearly ambient PM2.5 concentrations using the method developed by van Donkelaar et al7 to estimate the global distribution of ambient PM2.5. This resulted in estimates of a long-term average level of PM2.5 exposure. According to a previous validation study, the estimated PM2.5 had an expected 1-sigma uncertainty of 1 μg/m3+25%.7 The community of the participants was geocoded to match the PM2.5 concentration. Community was defined differently among the 6 countries; it is the township or community in China, an enumeration area in Ghana and South Africa, a village or census enumeration block in India, the Basic Geo-Statistical Area in Mexico, and an atenum in Russia. The 3-year average PM2.5 concentration before the survey was assigned and used in the main regression analyses.

Covariates

Physical activity was categorized into 3 levels (low, moderate, and high) based on the intensity, duration of the physical activities during work, transport activities to and from places, and recreational/leisure time activities.8

Consumption of fruit and vegetables was measured as the number of servings on a typical day. For the stratified analysis, the consumption of fruit and vegetables was distinguished as either sufficient or insufficient intake. We classified 2 or more servings of fruit per day as sufficient consumption, whereas ≥3.5 servings of vegetables per day were considered sufficient intake.9

Participants were also asked their most frequently used fuel for domestic cooking, as well as whether a domestic ventilation apparatus exists (chimney, exhaust hoods) while cooking. Two fuel types were used: clean fuels (electricity and natural gas) and unclean fuels (coal, wood, dung, and agricultural residues). Alcohol consumption was categorized into 2 broad groups: nondrinkers and drinkers. Tobacco consumption was also grouped into ever smoked and never smoked. Weight and height were measured to calculate the body mass index, expressed as weight/height2 (kg/m2). Marital status was divided into married (currently married or cohabiting) and unmarried (never married, separated, divorced, or widowed). Household income was categorized into 2 levels (low or high) using median income as the threshold.

We also measured the blood pressure of each participant. Hypertension was defined as systolic blood pressure ≥140 mm Hg, or diastolic blood pressure ≥90 mm Hg, or current treatment of hypertension with antihypertensive medication within the last 2 weeks before the interview.10

We also obtained information for several country-level indicators. The gross domestic product per capita was obtained from the Central Intelligence Agency’s World Factbook.11 We obtained the percentage of urban population, healthcare expenditure per capita, and the Gini coefficient, which measured income inequality with values ranging from 0 (equality) to 1 (inequality), from the World Bank’s World Development Indicators.12

Statistical Analysis

We conducted a 3-level logistic regression model, with participants, community, and country being the first-, second-, and third-level units. After the univariate analyses, multivariate models were fit to control for various potential confounders, such as age, sex, body mass index, consumption of fruit and vegetables, smoking, alcohol drinking, physical activity, education, annual household income, domestic fuel type and ventilation, hypertension, and antihypertensive medication.

We reported the effect estimates for each 10 µg/m3 increase in ambient PM2.5. We also explored the effect estimates by different levels of PM2.5 according to its distribution: low level (<14.33 µg/m3), moderate level (14.33–27.83 µg/m3), and high level (>27.83 µg/m3). We also used a natural spline smoothing function to examine the concentration–response relationship between exposure to PM2.5 and stroke.13

We also conducted sensitivity analyses to examine the robustness of the effect estimates. Specifically, we used the average PM2.5 concentration of 1, 2, 4, and 5 years before the survey to examine whether the results changed.

Subgroup Analysis

To examine effect modification, we performed stratified analyses for several factors: sex, age group (<60 years and ≥60 years), smoking (ever-smokers and never-smokers), physical activity (low, moderate, and high), and consumption of fruit and vegetables (sufficient and insufficient). The statistical difference between the subgroups was examined by including an interaction term of PM2.5 and the potential effect modifier in the model.14

Estimating Attributable Stroke Risk

We estimated the stroke burden attributable to ambient PM2.5 using 2 metrics, attributable cases and population attributable fraction.15 The concentration (25 μg/m3) set by WHO’s Air Quality Guidelines was used as the reference. The formula can be specified as:

where baseline prevalence is the stroke prevalence among participants exposed to reference PM2.5 concentration (25 μg/m3), which was 2.27% (23/1011); β is the coefficient of PM2.5–stroke association; △PC is the difference between the observed PM2.5 concentration and the reference concentration. Overall cases are the total stroke cases.

All the analyses were conducted using R software. A P value <0.05 was considered statistically significant.

Results

A total of 45 625 participants were included in this study (Table I in the online-only Data Supplement). The mean PM2.5 concentration in the 6 countries was 23.09 μg/m3. China and India had the highest PM2.5 concentration (32.79 μg/m3 and 30.69 μg/m3), whereas South Africa had the lowest level (5.93 μg/m3). The average age was 58.3 years, ranging from 50.0 years in India to 63.1 years in Mexico.

The demographic characteristics were presented in Table 1. Among the participants, 1239 (2.72%) were identified as stroke cases. Participants with stroke were significantly older than nonstroke respondents (66.9 versus 57.8 years), exposed to higher PM2.5 levels (23.24 versus 22.01 µg/m3), had higher body mass index (25.98 versus 24.21 kg/m2), were more likely to be female, unmarried, drinkers, have higher household income, and use clean fuels. Nonstroke respondents were more likely to live in rural areas, have higher physical activity level, and use ventilation at home. There was no significant difference in smoking status and consumption of fruit and vegetables between the 2 groups.

Comparison of Sociodemographic and Major Risk Factors Between Stroke and Nonstroke Participants

Table 2 showed the associations between PM2.5 and stroke. For each 10 µg/m3 increase, the odds ratio (OR) was 1.13 (95% confidence interval [CI], 1.04–1.22) in the univariate model and remained significant after adjusting for confounding factors (adjusted OR=1.13; 95% CI, 1.05–1.22). Using the ordinal levels of PM2.5, we found increasing risks with higher exposure levels (P for trend: 0.04 and 0.01 in univariate and multivariate models); compared with the low level, the OR for the high level was 1.53 (95% CI, 1.12–2.10). The smoothing curves of the relationships between ambient PM2.5 and stroke, while not perfect, suggested approximately linear relationships for the overall population (Figure), as well as for males (Figure I in the online-only Data Supplement) and females (Figure II in the online-only Data Supplement).

The concentration–response curves for the long-term effects of ambient PM2.5 on stroke for overall population 6 low- and middle-income countries.

The stratified analyses suggested that smoking status, physical activity, and consumption of fruit and vegetables might be important effect modifiers of the association between PM2.5 and stroke (Table 3). We observed a stronger effect among never-smokers (OR=1.16; 95% CI, 1.05–1.27) than among ever-smokers (OR=1.07; 95% CI, 0.96–1.19). It appeared that higher levels of physical activity could enhance the association. We observed a greater effect among participants with high levels of physical activity (OR=1.20; 95% CI, 1.07–1.35) than those with low and moderate physical activities. Consumption of fruit and vegetables might mitigate the harmful effects of PM2.5, the association was only significant among participants with lower consumption of fruit (OR=1.13; 95% CI, 1.05–1.21) and lower intake of vegetables (OR=1.17; 95% CI, 1.08–1.27). We did not find any significant effects among those with higher consumption of fruit or vegetables. No significant differences were observed in the stratified analyses in terms of sex or age.

Stratified Analyses for Stroke Associated With Each 10 μg/m3 Increase in Ambient PM2.5

Table 4 displayed the stroke burden attributable to ambient PM2.5. For all the participants, the population attributable risk because of ambient PM2.5 higher than 25 μg/m3 was 7.11% (95% CI, 2.45%–12.66%), corresponding to 88 (95% CI, 30–157) stroke cases. Comparable estimates were observed in the subpopulations stratified by sex and age group. For instance, the population attributable risk was 6.75% (95% CI, 1.59%–13.11%) among males, 6.36% (95% CI, 0.08%–14.27%) among females, and the attributable stroke cases were 41 (95% CI, 10–79) among males, and 41 (95% CI, 1–91) among females. We estimated that 3.86% (95% CI, 0.00%–9.68%) and 6.55% (95% CI, 1.27%–12.99%) of the stroke cases could be prevented among participants <60 years and ≥60 years, respectively.

The sensitivity analyses suggested a robust effect (see Table II in the online-only Data Supplement). After adjusting for country-level covariates, we found a significant association between PM2.5 and stroke with an OR of 1.14 (95% CI, 1.05–1.25). And when using average PM2.5 concentrations of 1, 2, 4, and 5 years before the survey, the analyses also yielded similar results.

Discussion

We observed a significant association between ambient PM2.5 and stroke in 6 low- and middle-income countries. We found a higher effect estimate among never-smokers and those with higher levels of physical activity, while consumption of fruit and vegetables may alleviate the association; we further estimated that ≈7% of the stroke cases could be attributable to ambient PM2.5 in the study population.

The observed association between PM2.5 and stroke was of comparable magnitude with some previous studies. Using the Women’s Health Initiative Observational Study, the investigators found a hazard ratio of 1.28 for stroke associated with 10 µg/m3 increases in PM2.5.2 Similarly, one study compared the air pollution levels in 1030 census enumeration districts to mortality and hospital admissions from stroke in Sheffield, United Kingdom, the rate ratios for the highest quintile of PM10 compared with the lowest quintile were 1.33 for mortality and 1.13 for hospital admissions.16 A significant association between PM10 and cerebrovascular mortality was also reported in one study in Shenyang, China.17 Whereas the California Teachers Study Cohort reported nonsignificant associations of long-term PM2.5 with stroke, but that study found a significant association between PM10 and stroke.18

The mechanisms by which ambient PM2.5 may increase the risk of stroke remained unclear. Long-term exposure to PM2.5 may cause chronic pulmonary and systemic oxidative stress, inflammation, and alterations in heart rate variability, which were critical and well-documented factors of endothelial dysfunction, vasoconstriction, accelerated atherosclerosis, and increased vulnerability to plaque rupture at vasculature level and coagulation and thrombosis at blood tissue level. These processes in turn were critical for the development of stroke.19,20 The fine particles have the ability to penetrate the blood–brain barrier and subsequently result in chronic inflammation and oxidative stress within the neural cells.2

Never-smokers were found to have a stronger stroke effect in this study, which should be interpreted cautiously. When the study was conducted in 2007, never-smokers were exposed to ubiquitous environmental tobacco smoke (ETS). Approximately 70% of nonsmokers in China were exposed to ETS, and 52% of nonsmokers in India were exposed to ETS at home and 29% in public places.21 Exposure to ETS could lead to intake of more harmful and higher dose of chemicals, which may cause significant adverse effects on stroke among the never-smokers.22 ETS exposure may have confounded the adverse effects from exposure to PM2.5.23,24 Unfortunately, exposure to ETS could not be quantified in this study owing to lack of time-activity data, which limited our ability to control for ETS in the model analyses. Furthermore, genetic differences between smokers and nonsmokers may make nonsmokers more susceptible to environmental exposure.25

During outdoor exercises, one may be exposed to more air pollutants because of increased breathing rates and intensity.3 Therefore, there is increasing concern about the trade-off between the health benefits of physical activity and potential health hazards from increased air pollution exposures.26 We observed a greater association between PM2.5 and stroke for high levels of physical activity. A few exposure studies also found reduced lung function associated with exercises in highly polluted areas.27 A literature review suggested that air pollution exposures during exercise may inhibit the beneficial effect of physical exercise on brain health.28 The underlying mechanism might be that the increased inhalation and deposition of air pollutants in the body of physically active people may amplify the harmful effects of air pollution.27

Our results suggested that higher consumption of fruit and vegetables could mitigate the stroke effects of ambient PM2.5, which was consistent with the hypothesized biological pathways, mainly related to neuroinflammation and peroxidation through oxidative stress.1 One Spanish study and a Danish cohort study also reported that intake of fruit and vegetables was associated with decreased mental health effects of air pollution.29,30 One study from Detroit also reported that antioxidant intake may protect against cardiovascular effects of PM2.5.31 Taken together, our study supported an intervention through increasing intake of fruit and vegetables to reduce the adverse effects of ambient PM2.5 exposure.

This study estimated the stroke burden attributable to ambient PM2.5 in low- and middle-income countries. Most of previous studies have mainly quantified the association between air pollution and health outcomes. Compared with the indicators of the association, the attributable risk may provide more information of public health significance.32 A similar approach has been applied in other studies. For example, we recently estimated that ≈3.8% of nonaccidental deaths could be attributable to ambient PM2.5 in 6 Chinese cities.33 Cohen et al34 reported that ≈0.8 million (1.2%) premature deaths globally could be prevented if the ambient PM2.5 standard was attained.

A few limitations should be noted. As a cross-sectional study, we cannot establish a causal relationship. With relatively few stroke cases in this study (1239), we cannot exclude the possibility of a chance finding. In addition, we used satellite-based estimates of PM2.5 concentrations as the exposure proxy. We were unable to have more detailed information in exposure assessment, such as the respondents’ activity patterns and detailed living address, time spent in traffic and indoors. All these contributed somewhat to exposure uncertainty and might have resulted in exposure misclassification. However, the model used to estimate PM2.5 concentrations has been validated7 and applied in previous studies.35 Finally, we did not have data on some important covariates, such as environmental tobacco smoking, meteorologic factors, and other air pollutants, which did not allow us to adjust for potential confounding in the model.

Conclusions

Our study supports that long-term exposure to ambient PM2.5 is associated with risk of stroke and responsible for remarkable stroke burden in low- and middle-income countries. High physical activity may enhance this effect, whereas consumption of fruit and vegetables may mitigate it.

Acknowledgments

We thank all the participants in the 6 countries for their participation and continued support.

Sources of Funding

This study was funded by World Health Organization, the US National Institute on Aging through Interagency Agreements (OGHA 04034785; YA1323-08-CN-0020; Y1-AG-1005-01), and a research grant (R01-AG034479); work in China was partially funded by the Science and Technology Commission of Shanghai Municipality (10XD1403600) and the Health Fields Specific Research Grant (201202012).

; National Heart, Lung, and Blood Institute Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure; National High Blood Pressure Education Program Coordinating Committee. The Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure: the JNC 7 report.JAMA. 2003;289:2560–2572. doi: 10.1001/jama.289.19.2560.